1) Removed support for parameter lists;

2) added wrappers for optimizer and schedule;
3) changed parameters for losses.

config/config.py

@@ -1,29 +1,41 @@
 import json
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, Optional
 from pydantic import BaseModel
 
 from .dataset_config import DatasetConfig
 
-class Config(BaseModel):
-    model: Dict[str, Union[BaseModel, List[BaseModel]]]
-    dataset_config: DatasetConfig
-    criterion: Optional[Dict[str, Union[BaseModel, List[BaseModel]]]] = None
-    optimizer: Optional[Dict[str, Union[BaseModel, List[BaseModel]]]] = None
-    scheduler: Optional[Dict[str, Union[BaseModel, List[BaseModel]]]] = None
 
-    @staticmethod
-    def __dump_field(value: Any) -> Any:
+__all__ = ["Config", "ComponentConfig"]
+
+
+class ComponentConfig(BaseModel):
+    name: str
+    params: BaseModel
+
+    def dump(self) -> Dict[str, Any]:
         """
-        Recursively dumps a field if it is a BaseModel or a list/dict of BaseModels.
+        Recursively serializes the component into a dictionary.
+
+        Returns:
+            dict: A dictionary containing the component name and its serialized parameters.
         """
-        if isinstance(value, BaseModel):
-            return value.model_dump()
-        elif isinstance(value, list):
-            return [Config.__dump_field(item) for item in value]
-        elif isinstance(value, dict):
-            return {k: Config.__dump_field(v) for k, v in value.items()}
+        if isinstance(self.params, BaseModel):
+            params_dump = self.params.model_dump()
         else:
-            return value
+            params_dump = self.params
+        return {
+            "name": self.name,
+            "params": params_dump
+        }
+        
+
+
+class Config(BaseModel):
+    model: ComponentConfig
+    dataset_config: DatasetConfig
+    criterion: Optional[ComponentConfig] = None
+    optimizer: Optional[ComponentConfig] = None
+    scheduler: Optional[ComponentConfig] = None
 
     def save_json(self, file_path: str, indent: int = 4) -> None:
         """
@@ -34,15 +46,15 @@ class Config(BaseModel):
             indent (int): Indentation level for the JSON file.
         """
         config_dump = {
-            "model": self.__dump_field(self.model),
+            "model": self.model.dump(),
             "dataset_config": self.dataset_config.model_dump()
         }
         if self.criterion is not None:
-            config_dump.update({"criterion": self.__dump_field(self.criterion)})
+            config_dump["criterion"] = self.criterion.dump()
         if self.optimizer is not None:
-            config_dump.update({"optimizer": self.__dump_field(self.optimizer)})
+            config_dump["optimizer"] = self.optimizer.dump()
         if self.scheduler is not None:
-            config_dump.update({"scheduler": self.__dump_field(self.scheduler)})
+            config_dump["scheduler"] = self.scheduler.dump()
         
         with open(file_path, "w", encoding="utf-8") as f:
             f.write(json.dumps(config_dump, indent=indent))
@@ -67,16 +79,15 @@ class Config(BaseModel):
         dataset_config = DatasetConfig(**data.get("dataset_config", {}))
 
         # Helper function to parse registry fields.
-        def parse_field(field_data: Dict[str, Any], registry_getter) -> Dict[str, Union[BaseModel, List[BaseModel]]]:
-            result = {}
-            for key, value in field_data.items():
-                expected = registry_getter(key)
-                # If the registry returns a tuple, then we expect a list of dictionaries.
-                if isinstance(expected, tuple):
-                    result[key] = [cls_param(**item) for cls_param, item in zip(expected, value)]
-                else:
-                    result[key] = expected(**value)
-            return result
+        def parse_field(component_data: Dict[str, Any], registry_getter) -> Optional[ComponentConfig]:
+            name = component_data.get("name")
+            params_data = component_data.get("params", {})
+            
+            if name is not None:
+                expected = registry_getter(name)
+                params = expected(**params_data)
+                return ComponentConfig(name=name, params=params)
+            return None
 
         from core import (
             ModelRegistry, CriterionRegistry, OptimizerRegistry, SchedulerRegistry
@@ -87,6 +98,9 @@ class Config(BaseModel):
         parsed_optimizer = parse_field(data.get("optimizer", {}), lambda key: OptimizerRegistry.get_optimizer_params(key))
         parsed_scheduler = parse_field(data.get("scheduler", {}), lambda key: SchedulerRegistry.get_scheduler_params(key))
 
+        if parsed_model is None:
+            raise ValueError('Failed to load model information')
+
         return cls(
             model=parsed_model,
             dataset_config=dataset_config,

config/dataset_config.py

@@ -98,6 +98,10 @@ class DatasetTrainingConfig(BaseModel):
           - If is_split is True, validates pre_split (train_dir must be non-empty and exist; if provided, valid_dir and test_dir must exist).
           - If is_split is False, validates split (all_data_dir must be non-empty and exist).
         """
+        if any(isinstance(s, float) for s in (self.train_size, self.valid_size, self.test_size)):
+            if (self.train_size + self.valid_size + self.test_size) > 1:
+                raise ValueError("The total sample size with dynamically defined sizes must be <= 1")
+        
         if not self.is_split:
             if not self.split.all_data_dir:
                 raise ValueError("When is_split is False, all_data_dir must be provided and non-empty in pre_split")

core/losses/__init__.py

@@ -5,12 +5,12 @@ from .base import BaseLoss
 from .ce import CrossEntropyLoss, CrossEntropyLossParams
 from .bce import BCELoss, BCELossParams
 from .mse import MSELoss, MSELossParams
-from .mse_with_bce import BCE_MSE_Loss
+from .mse_with_bce import BCE_MSE_Loss, BCE_MSE_LossParams
 
 __all__ = [
-    "CriterionRegistry",
+    "CriterionRegistry", "BaseLoss",
     "CrossEntropyLoss", "BCELoss", "MSELoss", "BCE_MSE_Loss",
-    "CrossEntropyLossParams", "BCELossParams", "MSELossParams"
+    "CrossEntropyLossParams", "BCELossParams", "MSELossParams", "BCE_MSE_LossParams"
 ]
 
 class CriterionRegistry:
@@ -31,7 +31,7 @@ class CriterionRegistry:
         },
         "BCE_MSE_Loss": {
             "class": BCE_MSE_Loss,
-            "params": (BCELossParams, MSELossParams),
+            "params": BCE_MSE_LossParams,
         },
     }
     
@@ -73,7 +73,7 @@ class CriterionRegistry:
         return entry["class"]
     
     @classmethod
-    def get_criterion_params(cls, name: str) -> Union[Type[BaseModel], Tuple[Type[BaseModel]]]:
+    def get_criterion_params(cls, name: str) -> Type[BaseModel]:
         """
         Retrieves the loss function parameter class (or classes) by name (case-insensitive).
         
@@ -81,7 +81,7 @@ class CriterionRegistry:
             name (str): Name of the loss function.
         
         Returns:
-            Union[Type[BaseModel], Tuple[Type[BaseModel]]]: The loss function parameter class or a tuple of parameter classes.
+            Type[BaseModel]: The loss function parameter class.
         """
         entry = cls.__get_entry(name)
         return entry["params"]

core/losses/base.py

@@ -1,6 +1,7 @@
 import abc
 import torch
 import torch.nn as nn
+from pydantic import BaseModel
 from typing import Dict, Any, Optional
 from monai.metrics.cumulative_average import CumulativeAverage
 
@@ -8,9 +9,7 @@ from monai.metrics.cumulative_average import CumulativeAverage
 class BaseLoss(abc.ABC):
     """Custom loss function combining BCEWithLogitsLoss and MSE losses for cell recognition and distinction."""
 
-    def __init__(self):
-        """
-        """
+    def __init__(self, params: Optional[BaseModel] = None):
         super().__init__()
         
         

core/losses/bce.py

@@ -9,27 +9,26 @@ class BCELossParams(BaseModel):
     """
     model_config = ConfigDict(frozen=True)
     
+    with_logits: bool = False
+    
     weight: Optional[List[Union[int, float]]] = None  # Sample weights
     reduction: Literal["none", "mean", "sum"] = "mean"  # Reduction method
     pos_weight: Optional[List[Union[int, float]]] = None  # Used only for BCEWithLogitsLoss
 
-    def asdict(self, with_logits: bool = False) -> Dict[str, Any]:
+    def asdict(self) -> Dict[str, Any]:
         """
         Returns a dictionary of valid parameters for `nn.BCEWithLogitsLoss` and `nn.BCELoss`.
 
         - If `with_logits=False`, `pos_weight` is **removed** to avoid errors.
         - Ensures only the valid parameters are passed based on the loss function.
 
-        Args:
-            with_logits (bool): If `True`, includes `pos_weight` (for `nn.BCEWithLogitsLoss`).
-                               If `False`, removes `pos_weight` (for `nn.BCELoss`).
-
         Returns:
             Dict[str, Any]: Filtered dictionary of parameters.
         """
         loss_kwargs = self.model_dump()
-        if not with_logits:
+        if not self.with_logits:
             loss_kwargs.pop("pos_weight", None)  # Remove pos_weight if using BCELoss
+            loss_kwargs.pop("with_logits", None)
         
         weight = loss_kwargs.get("weight")
         pos_weight = loss_kwargs.get("pos_weight")
@@ -48,15 +47,16 @@ class BCELoss(BaseLoss):
     Custom loss function wrapper for `nn.BCELoss and nn.BCEWithLogitsLoss` with tracking of loss metrics.
     """
 
-    def __init__(self, bce_params: Optional[BCELossParams] = None, with_logits: bool = False):
+    def __init__(self, params: Optional[BCELossParams] = None):
         """
         Initializes the loss function with optional BCELoss parameters.
 
         Args:
-            bce_params (Optional[Dict[str, Any]]): Parameters for nn.BCELoss (default: None).
+            params (Optional[Dict[str, Any]]): Parameters for nn.BCELoss (default: None).
         """
-        super().__init__()
-        _bce_params = bce_params.asdict(with_logits=with_logits) if bce_params is not None else {}
+        super().__init__(params=params)
+        with_logits = params.with_logits if params is not None else False
+        _bce_params = params.asdict() if params is not None else {}
         
         # Initialize loss functions with user-provided parameters or PyTorch defaults
         self.bce_loss = nn.BCEWithLogitsLoss(**_bce_params) if with_logits else nn.BCELoss(**_bce_params)

core/losses/ce.py

@@ -36,15 +36,15 @@ class CrossEntropyLoss(BaseLoss):
     Custom loss function wrapper for `nn.CrossEntropyLoss` with tracking of loss metrics.
     """
 
-    def __init__(self, ce_params: Optional[CrossEntropyLossParams] = None):
+    def __init__(self, params: Optional[CrossEntropyLossParams] = None):
         """
         Initializes the loss function with optional CrossEntropyLoss parameters.
 
         Args:
-            ce_params (Optional[Dict[str, Any]]): Parameters for nn.CrossEntropyLoss (default: None).
+            params (Optional[Dict[str, Any]]): Parameters for nn.CrossEntropyLoss (default: None).
         """
-        super().__init__()
-        _ce_params = ce_params.asdict() if ce_params is not None else {}
+        super().__init__(params=params)
+        _ce_params = params.asdict() if params is not None else {}
         
         # Initialize loss functions with user-provided parameters or PyTorch defaults
         self.ce_loss = nn.CrossEntropyLoss(**_ce_params)

core/losses/mse.py

@@ -27,15 +27,15 @@ class MSELoss(BaseLoss):
     Custom loss function wrapper for `nn.MSELoss` with tracking of loss metrics.
     """
 
-    def __init__(self, mse_params: Optional[MSELossParams] = None):
+    def __init__(self, params: Optional[MSELossParams] = None):
         """
         Initializes the loss function with optional MSELoss parameters.
 
         Args:
-            mse_params (Optional[MSELossParams]): Parameters for `nn.MSELoss` (default: None).
+            params (Optional[MSELossParams]): Parameters for `nn.MSELoss` (default: None).
         """
-        super().__init__()
-        _mse_params = mse_params.asdict() if mse_params is not None else {}
+        super().__init__(params=params)
+        _mse_params = params.asdict() if params is not None else {}
 
         # Initialize MSE loss with user-provided parameters or PyTorch defaults
         self.mse_loss = nn.MSELoss(**_mse_params)

core/losses/mse_with_bce.py

@@ -2,42 +2,59 @@ from .base import *
 from .bce import BCELossParams
 from .mse import MSELossParams
 
+from pydantic import BaseModel, ConfigDict
+
+
+class BCE_MSE_LossParams(BaseModel):
+    """
+    Class for handling parameters for `nn.MSELoss` with `nn.BCELoss`.
+    """
+    model_config = ConfigDict(frozen=True)
+    
+    num_classes: int = 1
+    bce_params: BCELossParams = BCELossParams()
+    mse_params: MSELossParams = MSELossParams()
+    
+    def asdict(self) -> Dict[str, Any]:
+        """
+        Returns a dictionary of valid parameters for `nn.BCELoss` and `nn.MSELoss`.
+
+        Returns:
+            Dict[str, Any]: Dictionary of parameters.
+        """
+        
+        return {
+            "num_classes": self.num_classes,
+            "bce_params": self.bce_params.asdict(),
+            "mse_params": self.mse_params.asdict()
+        }
+    
 
 class BCE_MSE_Loss(BaseLoss):
     """
     Custom loss function combining BCE (with or without logits) and MSE losses for cell recognition and distinction.
     """
 
-    def __init__(
-        self,
-        num_classes: int,
-        bce_params: Optional[BCELossParams] = None,
-        mse_params: Optional[MSELossParams] = None,
-        bce_with_logits: bool = False,
-    ):
+    def __init__(self, params: Optional[BCE_MSE_LossParams]):
         """
         Initializes the loss function with optional BCE and MSE parameters.
-
-        Args:
-            num_classes (int): Number of output classes, used for target shifting.
-            bce_params (Optional[BCELossParams]): Parameters for BCEWithLogitsLoss or BCELoss (default: None).
-            mse_params (Optional[MSELossParams]): Parameters for MSELoss (default: None).
-            bce_with_logits (bool): If True, uses BCEWithLogitsLoss; otherwise, uses BCELoss.
         """
-        super().__init__()
+        super().__init__(params=params)
+
+        _params = params if params is not None else BCE_MSE_LossParams()
 
-        self.num_classes = num_classes
+        self.num_classes = _params.num_classes
 
         # Process BCE parameters
-        _bce_params = bce_params.asdict(bce_with_logits) if bce_params is not None else {}
+        _bce_params = _params.bce_params.asdict()
 
         # Choose BCE loss function
         self.bce_loss = (
-            nn.BCEWithLogitsLoss(**_bce_params) if bce_with_logits else nn.BCELoss(**_bce_params)
+            nn.BCEWithLogitsLoss(**_bce_params) if _params.bce_params.with_logits else nn.BCELoss(**_bce_params)
         )
 
         # Process MSE parameters
-        _mse_params = mse_params.asdict() if mse_params is not None else {}
+        _mse_params = _params.mse_params.asdict()
 
         # Initialize MSE loss
         self.mse_loss = nn.MSELoss(**_mse_params)

core/models/__init__.py

@@ -61,7 +61,7 @@ class ModelRegistry:
         return entry["class"]
     
     @classmethod
-    def get_model_params(cls, name: str) -> Union[Type[BaseModel], Tuple[Type[BaseModel]]]:
+    def get_model_params(cls, name: str) -> Type[BaseModel]:
         """
         Retrieves the model parameter class by name (case-insensitive).
         
@@ -69,7 +69,7 @@ class ModelRegistry:
             name (str): Name of the model.
         
         Returns:
-            Union[Type[BaseModel], Tuple[Type[BaseModel]]]: The model parameter class or a tuple of parameter classes.
+            Type[BaseModel]: The model parameter class.
         """
         entry = cls.__get_entry(name)
         return entry["params"]

core/optimizers/__init__.py

@@ -1,14 +1,15 @@
-import torch.optim as optim
 from pydantic import BaseModel
 from typing import Dict, Final, Tuple, Type, List, Any, Union
 
-from .adam import AdamParams
-from .adamw import AdamWParams
-from .sgd import SGDParams
+from .base import BaseOptimizer
+from .adam import AdamParams, AdamOptimizer
+from .adamw import AdamWParams, AdamWOptimizer
+from .sgd import SGDParams, SGDOptimizer
 
 __all__ = [
-    "OptimizerRegistry",
-    "AdamParams", "AdamWParams", "SGDParams"
+    "OptimizerRegistry", "BaseOptimizer",
+    "AdamParams", "AdamWParams", "SGDParams",
+    "AdamOptimizer", "AdamWOptimizer", "SGDOptimizer"
 ]
 
 class OptimizerRegistry:
@@ -17,15 +18,15 @@ class OptimizerRegistry:
     # Single dictionary storing both optimizer classes and parameter classes.
     __OPTIMIZERS: Final[Dict[str, Dict[str, Type[Any]]]] = {
         "SGD": {
-            "class": optim.SGD,
+            "class": SGDOptimizer,
             "params": SGDParams,
         },
         "Adam": {
-            "class": optim.Adam,
+            "class": AdamOptimizer,
             "params": AdamParams,
         },
         "AdamW": {
-            "class": optim.AdamW,
+            "class": AdamWOptimizer,
             "params": AdamWParams,
         },
     }
@@ -54,7 +55,7 @@ class OptimizerRegistry:
         return cls.__OPTIMIZERS[original_key]
     
     @classmethod
-    def get_optimizer_class(cls, name: str) -> Type[optim.Optimizer]:
+    def get_optimizer_class(cls, name: str) -> Type[BaseOptimizer]:
         """
         Retrieves the optimizer class by name (case-insensitive).
         
@@ -62,13 +63,13 @@ class OptimizerRegistry:
             name (str): Name of the optimizer.
         
         Returns:
-            Type[optim.Optimizer]: The optimizer class.
+            Type[BaseOptimizer]: The optimizer class.
         """
         entry = cls.__get_entry(name)
         return entry["class"]
     
     @classmethod
-    def get_optimizer_params(cls, name: str) -> Union[Type[BaseModel], Tuple[Type[BaseModel]]]:
+    def get_optimizer_params(cls, name: str) -> Type[BaseModel]:
         """
         Retrieves the optimizer parameter class by name (case-insensitive).
         
@@ -76,7 +77,7 @@ class OptimizerRegistry:
             name (str): Name of the optimizer.
         
         Returns:
-            Union[Type[BaseModel], Tuple[Type[BaseModel]]]: The optimizer parameter class or a tuple of parameter classes.
+            Type[BaseModel]: The optimizer parameter class.
         """
         entry = cls.__get_entry(name)
         return entry["params"]

core/optimizers/adam.py

@@ -1,6 +1,9 @@
-from typing import Any, Dict, Tuple
+import torch
+from torch import optim
+from typing import Any, Dict, Iterable, Optional, Tuple
 from pydantic import BaseModel, ConfigDict
 
+from .base import BaseOptimizer
 
 class AdamParams(BaseModel):
     """Configuration for `torch.optim.Adam` optimizer."""
@@ -14,4 +17,21 @@ class AdamParams(BaseModel):
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.Adam`."""
-        return self.model_dump()
+        return self.model_dump()
+    
+    
+class AdamOptimizer(BaseOptimizer):
+    """
+    Wrapper around torch.optim.Adam.
+    """
+
+    def __init__(self, model_params: Iterable[torch.nn.Parameter], optim_params: AdamParams):
+        """
+        Initializes the Adam optimizer with given parameters.
+
+        Args:
+            model_params (Iterable[Parameter]): Parameters to optimize.
+            optim_params (AdamParams): Optimizer parameters.
+        """
+        super().__init__(model_params, optim_params)
+        self.optim = optim.Adam(model_params, **optim_params.asdict())

core/optimizers/adamw.py

@@ -1,6 +1,10 @@
-from typing import Any, Dict, Tuple
+import torch
+from torch import optim
+from typing import Any, Dict, Iterable, Optional, Tuple
 from pydantic import BaseModel, ConfigDict
 
+from .base import BaseOptimizer
+
 class AdamWParams(BaseModel):
     """Configuration for `torch.optim.AdamW` optimizer."""
     model_config = ConfigDict(frozen=True)
@@ -13,4 +17,21 @@ class AdamWParams(BaseModel):
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.AdamW`."""
-        return self.model_dump()
+        return self.model_dump()
+    
+
+class AdamWOptimizer(BaseOptimizer):
+    """
+    Wrapper around torch.optim.AdamW.
+    """
+
+    def __init__(self, model_params: Iterable[torch.nn.Parameter], optim_params: AdamWParams):
+        """
+        Initializes the AdamW optimizer with given parameters.
+
+        Args:
+            model_params (Iterable[Parameter]): Parameters to optimize.
+            optim_params (AdamWParams): Optimizer parameters.
+        """
+        super().__init__(model_params, optim_params)
+        self.optim = optim.AdamW(model_params, **optim_params.asdict())

core/optimizers/base.py

@@ -0,0 +1,40 @@
+import torch
+import torch.optim as optim
+from pydantic import BaseModel
+from typing import Any, Iterable, Optional
+
+
+class BaseOptimizer:
+    """Custom loss function combining BCEWithLogitsLoss and MSE losses for cell recognition and distinction."""
+
+    def __init__(self, model_params: Iterable[torch.nn.Parameter], optim_params: BaseModel):
+        super().__init__()
+        self.optim: Optional[optim.Optimizer] = None
+        
+
+    def zero_grad(self, set_to_none: bool = True) -> None:
+        """
+        Clears the gradients of all optimized tensors.
+
+        Args:
+            set_to_none (bool): If True, sets gradients to None instead of zero.
+                                This can reduce memory usage and improve performance.
+                                (Introduced in PyTorch 1.7+)
+        """
+        if self.optim is not None:
+            self.optim.zero_grad(set_to_none=set_to_none)
+
+
+    def step(self, closure: Optional[Any] = None) -> Any:
+        """
+        Performs a single optimization step (parameter update).
+
+        Args:
+            closure (Optional[Callable]): A closure that reevaluates the model and returns the loss.
+                                          This is required for optimizers like LBFGS that need multiple forward passes.
+
+        Returns:
+            Any: The return value depends on the specific optimizer implementation.
+        """
+        if self.optim is not None:
+            return self.optim.step(closure=closure)

core/optimizers/sgd.py

@@ -1,6 +1,10 @@
-from typing import Any, Dict
+import torch
+from torch import optim
+from typing import Any, Dict, Iterable, Optional
 from pydantic import BaseModel, ConfigDict
 
+from .base import BaseOptimizer
+
 
 class SGDParams(BaseModel):
     """Configuration for `torch.optim.SGD` optimizer."""
@@ -14,4 +18,21 @@ class SGDParams(BaseModel):
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.SGD`."""
-        return self.model_dump()
+        return self.model_dump()
+    
+    
+class SGDOptimizer(BaseOptimizer):
+    """
+    Wrapper around torch.optim.SGD.
+    """
+
+    def __init__(self, model_params: Iterable[torch.nn.Parameter], optim_params: SGDParams):
+        """
+        Initializes the SGD optimizer with given parameters.
+
+        Args:
+            model_params (Iterable[Parameter]): Parameters to optimize.
+            optim_params (SGDParams): Optimizer parameters.
+        """
+        super().__init__(model_params, optim_params)
+        self.optim = optim.SGD(model_params, **optim_params.asdict())

core/schedulers/__init__.py

@@ -2,14 +2,16 @@ import torch.optim.lr_scheduler as lr_scheduler
 from typing import Dict, Final, Tuple, Type, List, Any, Union
 from pydantic import BaseModel
 
-from .step import StepLRParams
-from .multi_step import MultiStepLRParams
-from .exponential import ExponentialLRParams
-from .cosine_annealing import CosineAnnealingLRParams
+from .base import BaseScheduler
+from .step import StepLRParams, StepLRScheduler
+from .multi_step import MultiStepLRParams, MultiStepLRScheduler
+from .exponential import ExponentialLRParams, ExponentialLRScheduler
+from .cosine_annealing import CosineAnnealingLRParams, CosineAnnealingLRScheduler
 
 __all__ = [
-    "SchedulerRegistry",
-    "StepLRParams", "MultiStepLRParams", "ExponentialLRParams", "CosineAnnealingLRParams"
+    "SchedulerRegistry", "BaseScheduler",
+    "StepLRParams", "MultiStepLRParams", "ExponentialLRParams", "CosineAnnealingLRParams",
+    "StepLRScheduler", "MultiStepLRScheduler", "ExponentialLRScheduler", "CosineAnnealingLRScheduler"
 ]
 
 class SchedulerRegistry:
@@ -17,19 +19,19 @@ class SchedulerRegistry:
     
     __SCHEDULERS: Final[Dict[str, Dict[str, Type[Any]]]] = {
         "Step": {
-            "class": lr_scheduler.StepLR,
+            "class": StepLRScheduler,
             "params": StepLRParams,
         },
         "Exponential": {
-            "class": lr_scheduler.ExponentialLR,
+            "class": ExponentialLRScheduler,
             "params": ExponentialLRParams,
         },
         "MultiStep": {
-            "class": lr_scheduler.MultiStepLR,
+            "class": MultiStepLRScheduler,
             "params": MultiStepLRParams,
         },
         "CosineAnnealing": {
-            "class": lr_scheduler.CosineAnnealingLR,
+            "class": CosineAnnealingLRScheduler,
             "params": CosineAnnealingLRParams,
         },
     }
@@ -58,7 +60,7 @@ class SchedulerRegistry:
         return cls.__SCHEDULERS[original_key]
     
     @classmethod
-    def get_scheduler_class(cls, name: str) -> Type[lr_scheduler.LRScheduler]:
+    def get_scheduler_class(cls, name: str) -> Type[BaseScheduler]:
         """
         Retrieves the scheduler class by name (case-insensitive).
         
@@ -66,13 +68,13 @@ class SchedulerRegistry:
             name (str): Name of the scheduler.
         
         Returns:
-            Type[lr_scheduler.LRScheduler]: The scheduler class.
+            Type[BaseScheduler]: The scheduler class.
         """
         entry = cls.__get_entry(name)
         return entry["class"]
     
     @classmethod
-    def get_scheduler_params(cls, name: str) -> Union[Type[BaseModel], Tuple[Type[BaseModel]]]:
+    def get_scheduler_params(cls, name: str) -> Type[BaseModel]:
         """
         Retrieves the scheduler parameter class by name (case-insensitive).
         
@@ -80,7 +82,7 @@ class SchedulerRegistry:
             name (str): Name of the scheduler.
         
         Returns:
-            Union[Type[BaseModel], Tuple[Type[BaseModel]]]: The scheduler parameter class or a tuple of parameter classes.
+            Type[BaseModel]: The scheduler parameter class.
         """
         entry = cls.__get_entry(name)
         return entry["params"]

core/schedulers/base.py

@@ -0,0 +1,27 @@
+import torch.optim as optim
+from pydantic import BaseModel
+from typing import List, Optional
+
+
+class BaseScheduler:
+    """
+    Abstract base class for learning rate schedulers.
+    Wraps a PyTorch LR scheduler and provides a unified interface.
+    """
+
+    def __init__(self, optimizer: optim.Optimizer, params: BaseModel):
+        self.scheduler: Optional[optim.lr_scheduler.LRScheduler] = None
+
+    def step(self) -> None:
+        """
+        Performs a single scheduler step. This typically updates the learning rate
+        based on the current epoch or step count.
+        """
+        if self.scheduler is not None:
+            self.scheduler.step()
+
+    def get_last_lr(self) -> List[float]:
+        """
+        Returns the most recent learning rate(s).
+        """
+        return self.scheduler.get_last_lr() if self.scheduler else []

core/schedulers/cosine_annealing.py

@@ -1,5 +1,10 @@
 from typing import Any, Dict
 from pydantic import BaseModel, ConfigDict
+from torch import optim
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+from .base import BaseScheduler
+
 
 
 class CosineAnnealingLRParams(BaseModel):
@@ -8,7 +13,24 @@ class CosineAnnealingLRParams(BaseModel):
 
     T_max: int = 100        # Maximum number of iterations
     eta_min: float = 0.0    # Minimum learning rate
+    last_epoch: int = -1
+
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.lr_scheduler.CosineAnnealingLR`."""
         return self.model_dump()
+
+
+class CosineAnnealingLRScheduler(BaseScheduler):
+    """
+    Wrapper around torch.optim.lr_scheduler.CosineAnnealingLR.
+    """
+
+    def __init__(self, optimizer: optim.Optimizer, params: CosineAnnealingLRParams):
+        """
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            params (CosineAnnealingLRParams): Scheduler parameters.
+        """
+        super().__init__(optimizer, params)
+        self.scheduler = CosineAnnealingLR(optimizer, **params.asdict())

core/schedulers/exponential.py

@@ -1,5 +1,9 @@
 from typing import Any, Dict
 from pydantic import BaseModel, ConfigDict
+from torch import optim
+from torch.optim.lr_scheduler import ExponentialLR
+
+from .base import BaseScheduler
 
 
 class ExponentialLRParams(BaseModel):
@@ -7,7 +11,23 @@ class ExponentialLRParams(BaseModel):
     model_config = ConfigDict(frozen=True)
 
     gamma: float = 0.95 # Multiplicative factor of learning rate decay
+    last_epoch: int = -1
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.lr_scheduler.ExponentialLR`."""
-        return self.model_dump()
+        return self.model_dump()
+
+
+class ExponentialLRScheduler(BaseScheduler):
+    """
+    Wrapper around torch.optim.lr_scheduler.ExponentialLR.
+    """
+
+    def __init__(self, optimizer: optim.Optimizer, params: ExponentialLRParams):
+        """
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            params (ExponentialLRParams): Scheduler parameters.
+        """
+        super().__init__(optimizer, params)
+        self.scheduler = ExponentialLR(optimizer, **params.asdict())

core/schedulers/multi_step.py

@@ -1,5 +1,9 @@
 from typing import Any, Dict, Tuple
 from pydantic import BaseModel, ConfigDict
+from torch import optim
+from torch.optim.lr_scheduler import MultiStepLR
+
+from .base import BaseScheduler
 
 
 class MultiStepLRParams(BaseModel):
@@ -8,7 +12,23 @@ class MultiStepLRParams(BaseModel):
 
     milestones: Tuple[int, ...] = (30, 80)   # List of epoch indices for LR decay
     gamma: float = 0.1                     # Multiplicative factor of learning rate decay
+    last_epoch: int = -1
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.lr_scheduler.MultiStepLR`."""
-        return self.model_dump()
+        return self.model_dump()
+
+
+class MultiStepLRScheduler(BaseScheduler):
+    """
+    Wrapper around torch.optim.lr_scheduler.MultiStepLR.
+    """
+
+    def __init__(self, optimizer: optim.Optimizer, params: MultiStepLRParams):
+        """
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            params (MultiStepLRParams): Scheduler parameters.
+        """
+        super().__init__(optimizer, params)
+        self.scheduler = MultiStepLR(optimizer, **params.asdict())

core/schedulers/step.py

@@ -1,5 +1,9 @@
 from typing import Any, Dict
 from pydantic import BaseModel, ConfigDict
+from torch import optim
+from torch.optim.lr_scheduler import StepLR
+
+from .base import BaseScheduler
 
 
 class StepLRParams(BaseModel):
@@ -8,7 +12,24 @@ class StepLRParams(BaseModel):
 
     step_size: int = 30     # Period of learning rate decay
     gamma: float = 0.1      # Multiplicative factor of learning rate decay
+    last_epoch: int = -1
 
     def asdict(self) -> Dict[str, Any]:
         """Returns a dictionary of valid parameters for `torch.optim.lr_scheduler.StepLR`."""
-        return self.model_dump()
+        return self.model_dump()
+    
+
+
+class StepLRScheduler(BaseScheduler):
+    """
+    Wrapper around torch.optim.lr_scheduler.StepLR.
+    """
+
+    def __init__(self, optimizer: optim.Optimizer, params: StepLRParams):
+        """
+        Args:
+            optimizer (Optimizer): Wrapped optimizer.
+            params (StepLRParams): Scheduler parameters.
+        """
+        super().__init__(optimizer, params)
+        self.scheduler = StepLR(optimizer, **params.asdict())

generate_config.py

@@ -1,8 +1,7 @@
 import os
-from pydantic import BaseModel
-from typing import Any, Dict, Tuple, Type, Union, List
+from typing import Tuple
 
-from config.config import Config
+from config.config import *
 from config.dataset_config import DatasetConfig
 
 from core import (
@@ -10,18 +9,6 @@ from core import (
 )
 
 
-def instantiate_params(param: Any) -> Union[BaseModel, List[BaseModel]]:
-    """
-    Instantiates the parameter class(es) with default values.
-    
-    If 'param' is a tuple, instantiate each class and return a list of instances.
-    Otherwise, instantiate the single class and return the instance.
-    """
-    if isinstance(param, tuple):
-        return [cls() for cls in param]
-    else:
-        return param()
-
 def prompt_choice(prompt_message: str, options: Tuple[str, ...]) -> str:
     """
     Prompt the user with a list of options and return the selected option.
@@ -55,11 +42,11 @@ def main():
     model_options = ModelRegistry.get_available_models()
     chosen_model = prompt_choice("\nSelect a model:", model_options)
     model_param_class = ModelRegistry.get_model_params(chosen_model)
-    model_instance = instantiate_params(model_param_class)
+    model_instance = model_param_class()
 
     if is_training is False:
         config = Config(
-            model={chosen_model: model_instance},
+            model=ComponentConfig(name=chosen_model, params=model_instance),
             dataset_config=dataset_config
         )
         
@@ -71,27 +58,27 @@ def main():
         criterion_options = CriterionRegistry.get_available_criterions()
         chosen_criterion = prompt_choice("\nSelect a criterion:", criterion_options)
         criterion_param_class = CriterionRegistry.get_criterion_params(chosen_criterion)
-        criterion_instance = instantiate_params(criterion_param_class)
+        criterion_instance = criterion_param_class()
 
         # Prompt the user to select an optimizer.
         optimizer_options = OptimizerRegistry.get_available_optimizers()
         chosen_optimizer = prompt_choice("\nSelect an optimizer:", optimizer_options)
         optimizer_param_class = OptimizerRegistry.get_optimizer_params(chosen_optimizer)
-        optimizer_instance = instantiate_params(optimizer_param_class)
+        optimizer_instance = optimizer_param_class()
 
         # Prompt the user to select a scheduler.
         scheduler_options = SchedulerRegistry.get_available_schedulers()
         chosen_scheduler = prompt_choice("\nSelect a scheduler:", scheduler_options)
         scheduler_param_class = SchedulerRegistry.get_scheduler_params(chosen_scheduler)
-        scheduler_instance = instantiate_params(scheduler_param_class)
+        scheduler_instance = scheduler_param_class()
 
         # Assemble the overall configuration using the registry names as keys.
         config = Config(
-            model={chosen_model: model_instance},
+            model=ComponentConfig(name=chosen_model, params=model_instance),
             dataset_config=dataset_config,
-            criterion={chosen_criterion: criterion_instance},
-            optimizer={chosen_optimizer: optimizer_instance},
-            scheduler={chosen_scheduler: scheduler_instance}
+            criterion=ComponentConfig(name=chosen_criterion, params=criterion_instance),
+            optimizer=ComponentConfig(name=chosen_optimizer, params=optimizer_instance),
+            scheduler=ComponentConfig(name=chosen_scheduler, params=scheduler_instance)
         )
         
         # Construct a base filename from the selected registry names.

train.py

@@ -7,8 +7,4 @@ pprint(config, indent=4)
 
 print('\n\n')
 config = Config.load_json('/workspace/ext_data/projects/model-v/config/templates/predict/ModelV.json')
-pprint(config, indent=4)
-
-print('\n\n')
-config = Config.load_json('/workspace/ext_data/projects/model-v/config/templates/predict/ModelV_1.json')
 pprint(config, indent=4)